An improved ICM algorithm for remote sensing image segmentation

doi:10.6046/gtzyyg.2018.03.03

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Abstract

The traditional iterated conditional model(ICM)algorithm, when applied to remote sensing image segmentation, is easy to show discrete patches and isolated points. In view of this phenomenon, an improved ICM remote sensing segmentation algorithm is proposed which is based on Markov random field(MRF). First, the robust bilateral filter(BF)which is efficient in preserving edges and denoising was merged and used for the preprocessing of the remote sensing image, and then the Otsu algorithm was applied to obtaining the initial clusters. The algorithm could overcome some problems that occurred in the traditional K-means algorithm such as the inability in determining the number of clusters, difficulty in controlling algorithm complexities, and appearance of overlapping in the segmented regions. Next, the MRF was used to describe the pixel spatial correlation forming ICM remote sensing image segmentation algorithm with contextual information. By using remote sensing image data validation, the approach proposed in this paper realizes more reliable segmentation results in comparison with the traditional ICM algorithm.

Keywords iterated conditional model(ICM) Markov random field(MRF) Otsu bilateral filter(BF) image segmentation

TP751.1

Issue Date: 10 September 2018

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	Jun YANG
	Jianjie PEI

Cite this article:

Jun YANG,Jianjie PEI. An improved ICM algorithm for remote sensing image segmentation[J]. Remote Sensing for Land & Resources, 2018, 30(3): 18-25.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.03.03 OR https://www.gtzyyg.com/EN/Y2018/V30/I3/18

Fig.1 Execution process of ICM algorithm

Fig.2 Comparison of segmentation results of Grand Canyon aerial image by using different algorithms

Tab.1 Segmentation accuracy of different algorithms

Fig.3 Comparison of segmentation results of Komodo National Park image by using different algorithms

Fig.4 Comparison of segmentation results of Alajuela Lake multispectral image by using different algorithms

Fig.5 Comparison of segmentation results of Chara sand multispectral remote sensing image by using different algorithms

Fig.6 Relationship between Kappa coefficients and number of clusters

Fig.7 Comparison of segmentation results of Rakaia River aerial images by using different algorithms

Fig.8 Relationship between segmentation accuracy and number of clusters from aerial images of Rakaia River

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